DSI Distinguished Speaker Series highlights senior researchers who are applying data science to a broader scientific or academic expertise.

Hosted by DSI Postdoctoral Researchers.


Guest Speaker

Aaron Gilad Kusne,Research Scientist, Materials Measurement Science Division, National Institute of Standards & Technology (NIST); and Adjunct Associate Professor of Materials Science & Engineering, University of Maryland College Park

Moderated by:

  • Yevgeny Rakita Shlafstein, DSI Postdoctoral Research Scientist

Details

Monday, October 25 (2:00 PM – 3:00 PM ET) – Virtual


Abstract & Biography

Autonomous Materials Research and Discovery at NIST

The last few decades have seen significant advancements in materials research tools, allowing scientists to rapidly synthesis and characterize large numbers of samples – a major step toward high-throughput materials discovery. Autonomous research systems take the next step, placing synthesis and characterization under control of machine learning. For such systems, machine learning controls experiment design, execution, and analysis, thus accelerating knowledge capture while also reducing the burden on experts. Furthermore, physical knowledge can be built into the machine learning, reducing the expertise needed by users, with the promise of eventually democratizing science. In this talk I will discuss autonomous systems being developed at NIST with a particular focus on autonomous control over user facility measurement systems for materials characterization, exploration and discovery.

Bio: A. Gilad Kusne received his B.S., M.S., and Ph.D. degrees from Carnegie Mellon University. He is a Staff Scientist with the National Institute of Standards and Technology (NIST), Gaithersburg, Maryland, and an adjunct professor with the University of Maryland. His research is part of the White House’s Materials Genome Initiative at NIST, a project which aims to accelerate the discovery and optimization of advanced materials. He leads the machine learning team of an international, cross-disciplinary effort building autonomous research systems, with the goal of advancing solid state, soft, and biological materials. For these systems, machine learning performs experiment design, execution (in the lab and in silico), and analysis. For his work, he has been awarded the NIST Bronze Award (highest NIST award). He is also the lead founder and organizer of the annual Machine Learning for Materials Research Bootcamp and Workshop—educating next generation and mid-career material scientists in machine learning.